公开(公告)号：US11763154B1

公开(公告)日：2023-09-19

申请号：US16262677

申请日：2019-01-30

Applicant: Amazon Technologies, Inc.

Inventor： Hagay Lupesko ,  Anirudh Acharya ,  Lee Cheng-Che ,  Lai Wei ,  Kalyanee Chendke ,  Ankit Khedia ,  Vandana Kannan ,  Sandeep Krishnamurthy ,  Roshani Nagmote

IPC: G06N3/08 ,  G06N20/00 ,  G06T7/10 ,  G06F16/55 ,  G06N20/20 ,  G06N3/082 ,  G06F18/214

CPC classification number: G06N3/082 ,  G06F16/55 ,  G06F18/214 ,  G06N20/00 ,  G06N20/20 ,  G06T7/10

Abstract: Features related to systems and methods for automated generation of a machine learning model based in part on a pretrained model are described. The pretrained model is used as a starting point to augment and retrain according to client specifications. The identification of an appropriate pretrained model is based on the client specifications such as model inputs, model outputs, and similarities between the data used to train the models.

公开(公告)号：US11769035B1

公开(公告)日：2023-09-26

申请号：US16219751

申请日：2018-12-13

Applicant: Amazon Technologies, Inc.

Inventor： Lai Wei ,  Hagay Lupesko ,  Anirudh Acharya ,  Ankit Khedia ,  Sandeep Krishnamurthy ,  Cheng-Che Lee ,  Kalyanee Shriram Chendke ,  Vandana Kannan ,  Roshani Nagmote

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: Techniques are described automatically determining runtime configurations used to execute recurrent neural networks (RNNs) for training or inference. One such configuration involves determining whether to execute an RNN in a looped, or “rolled,” execution pattern or in a non-looped, or “unrolled,” execution pattern. Execution of an RNN using a rolled execution pattern generally consumes less memory resources than execution using an unrolled execution pattern, whereas execution of an RNN using an unrolled execution pattern typically executes faster. The configuration choice thus involves a time-memory tradeoff that can significantly affect the performance of the RNN execution. This determination is made automatically by a machine learning (ML) runtime by analyzing various factors such as, for example, a type of RNN being executed, the network structure of the RNN, characteristics of the input data to the RNN, an amount of computing resources available, and so forth.

公开(公告)号：US10949252B1

公开(公告)日：2021-03-16

申请号：US15895747

申请日：2018-02-13

Applicant: Amazon Technologies, Inc.

Inventor： Sandeep Krishnamurthy ,  Jiajie Chen ,  Jonathan Esterhazy ,  Naveen Mysore Nagendra Swamy ,  Ruofei Yu ,  Yao Wang ,  Roshani Nagmote ,  Hagay Lupesko ,  Vikram Madan

IPC: G06F9/48 ,  G06N20/00

Abstract: Techniques for benchmarking a machine learning model/algorithm are described. For example, in some instances a method includes generating an execution plan for benchmarking of at least one task corresponding to a machine learning model based on an identified machine learning model, identified training data, and at least one objective for the benchmarking job; receiving execution statistics about the execution of the task as a part of the benchmarking job according to the execution plan; and updating the execution plan based at least in part on the received execution statistics of the task.

公开(公告)号：US20230368028A1

公开(公告)日：2023-11-16

申请号：US18217929

申请日：2023-07-03

Applicant: Amazon Technologies, Inc.

Inventor： Hagay Lupesko ,  Anirudh Acharya ,  Cheng-Che Lee ,  Lai Wei ,  Kalyanee Chendke ,  Ankit Khedia ,  Vandana Kannan ,  Sandeep Krishnamurthy ,  Roshani Nagmote

IPC: G06N3/082 ,  G06N20/00 ,  G06N20/20 ,  G06T7/10 ,  G06F16/55 ,  G06F18/214

CPC classification number: G06N3/082 ,  G06N20/00 ,  G06N20/20 ,  G06T7/10 ,  G06F16/55 ,  G06F18/214

Abstract: Features related to systems and methods for automated generation of a machine learning model based in part on a pretrained model are described. The pretrained model is used as a starting point to augment and retrain according to client specifications. The identification of an appropriate pretrained model is based on the client specifications such as model inputs, model outputs, and similarities between the data used to train the models.

公开(公告)号：US10997409B1

公开(公告)日：2021-05-04

申请号：US16001618

申请日：2018-06-06

Applicant: Amazon Technologies, Inc.

Inventor： Sandeep Krishnamurthy ,  Rajankumar Singh ,  Aaron Markham ,  Lai Wei

IPC: G06K9/00 ,  G06K9/62 ,  G06T11/20 ,  G06N5/04

Abstract: Techniques are described for using machine learning (ML) models to create information technology (IT) infrastructures at a service provider network based on image of IT system architecture diagrams. To create IT system architecture diagrams, system architects often use tools ranging from pen and paper and whiteboards to various types of software-based drawing programs. Based on a user-provided image of an IT system architecture diagram (for example, a digital scan of a hand drawn system diagram, an image file created by a software-based drawing program, or the like), a service provider network uses one or more ML models to analyze the image to identify the constituent elements of the depicted IT system architecture and to create an infrastructure template that can be used to automatically provision corresponding computing resources at the service provider network.

公开(公告)号：US11423283B1

公开(公告)日：2022-08-23

申请号：US15933114

申请日：2018-03-22

Applicant: Amazon Technologies, Inc.

Inventor： Hagay Lupesko ,  Dominic Rajeev Divakaruni ,  Jonathan Esterhazy ,  Sandeep Krishnamurthy ,  Vikram Madan ,  Roshani Nagmote ,  Naveen Mysore Nagendra Swamy ,  Yao Wang

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/10

Abstract: Techniques for model adaptation are described. For example, a method of receiving a call to provide either a model variant or a model variant profile of a deep learning model, the call including desired performance of the deep learning model, a deep learning model identifier, and current edge device characteristics; comparing the received current edge device characteristics to available model variants and profiles based on the desired performance of the deep learning model to generate or select a model variant or profile, the available model variants and profiles determined by the model identifier; and sending the generated or selected model variant or profile to the edge device to use in inference is detailed.